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Classification of word levels with usage frequency, expert opinions and machine learning
Author(s) -
Sohsah Gihad N.,
Ünal Muhammed Esad,
Güzey Onur
Publication year - 2015
Publication title -
british journal of educational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.79
H-Index - 95
eISSN - 1467-8535
pISSN - 0007-1013
DOI - 10.1111/bjet.12338
Subject(s) - computer science , artificial intelligence , natural language processing , license , selection (genetic algorithm) , word (group theory) , resource (disambiguation) , linguistics , philosophy , computer network , operating system
Abstract Educational applications for language teaching can utilize the language levels of words to target proficiency levels of students. This paper and the accompanying data provide a methodology for making educational standard‐aligned language‐level predictions for all English words. The methodology involves expert opinions on language levels and extending these opinions to other words using machine learning and data from a large corpus. Common E uropean Framework for Languages ( CEFR ) level predictions for about 50 000 words, which can be readily used in educational applications, are also provided. For applications where the cost of misclassification varies, machine learning model parameters and algorithm selection must be adjusted. A large number of expert opinions taken from a survey with 30 practicing language teachers that can be used for this adjustment are also released. The overall methodology can be applied to low‐resource languages, where CEFR ‐level classifications may not exist, by adding a comparable survey and corpus. The data are released with a Creative Commons Attribution license to enable free mixing, sharing and even use in commercial applications.